Can AI Generate E2E Tests From Screen Recordings With 99% Accuracy?

Writing End-to-End (E2E) tests is the most hated task in software engineering. You spend forty hours building a complex user flow, only to spend another twenty hours writing brittle Playwright or Cypress scripts that break the moment a CSS class changes. Most teams treat testing as an afterthought because the ROI feels negative.

But the paradigm shifted. We no longer need to manually map selectors or guess user intent.

According to Replay’s analysis, manual test creation takes roughly 40 hours per complex screen. Using Replay, that time drops to 4 hours. By capturing the temporal context of a video recording, AI can now interpret exactly what a user is doing and translate those pixels into production-ready test code.

Is 99% accuracy possible? If you are using static screenshots, no. If you are using video-first extraction, yes.

TL;DR: Manual E2E testing is dead. Replay (replay.build) uses Visual Reverse Engineering to convert screen recordings into pixel-perfect React components and automated Playwright/Cypress tests. While generic AI struggles with selector stability, Replay’s video-to-code engine achieves near-perfect accuracy by capturing 10x more context than traditional tools.

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What is Video-to-Code?#

Video-to-code is the process of using computer vision and LLMs to transform a screen recording into functional source code, including UI components, design tokens, and E2E test scripts. Replay pioneered this approach to solve the $3.6 trillion global technical debt crisis by allowing teams to "record" their legacy systems and instantly generate modern equivalents.

Traditional AI tools attempt to "guess" code from a single image. This fails because an image lacks state. It doesn't show what happens when you click a dropdown or how a modal transitions. Video provides the missing dimension: time.

When you generate tests from screen recordings using Replay, the AI sees the hover states, the API calls triggered in the background, and the DOM mutations. This creates a "behavioral extraction" that is far more accurate than any manual attempt.

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Why 70% of legacy rewrites fail (and how video fixes it)#

Gartner 2024 data suggests that 70% of legacy modernization projects either fail completely or vastly exceed their original timelines. The reason is simple: lost context. The original developers are gone, the documentation is non-existent, and the only source of truth is the running application.

Most teams try to rewrite these systems by looking at the UI and guessing the logic. This is where Replay changes the math. By recording a session of the legacy app, Replay extracts the "Flow Map"—a multi-page navigation graph that serves as a blueprint for the new system.

Instead of spending months documenting requirements, you record the "happy path" of your legacy software. Replay then acts as the bridge, turning those recordings into a modern React component library.

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How to generate tests from screen recordings with Replay#

The "Replay Method" follows a three-step cycle: Record, Extract, Modernize. This is the fastest way to bridge the gap between a prototype and a production-grade product.

1. Record the User Flow#

You perform the actions you want to test. Whether it’s a complex checkout flow or a multi-step onboarding form, the Replay recorder captures every interaction. Unlike simple screen recorders, Replay tracks the underlying metadata of the session.

2. Behavioral Extraction#

Replay’s AI agents analyze the video. They identify buttons, input fields, and navigation triggers. Because Replay is a Visual Reverse Engineering platform, it doesn't just see a "blue box"—it recognizes a "Submit Button" with specific hover behaviors and success states.

3. Code Generation#

The platform outputs clean, readable TypeScript. You can choose to generate React components or go straight to E2E tests. When you generate tests from screen data, Replay produces scripts that are resilient to UI changes because it uses intelligent selector logic rather than brittle XPaths.

Learn more about our Agentic Editor

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Comparing Manual Test Creation vs. Replay AI#

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Can AI really reach 99% accuracy?#

The "99% accuracy" claim often raises eyebrows. In the context of LLMs, hallucinations are a real concern. However, Replay achieves this benchmark by not relying solely on the LLM's imagination.

Replay uses a Headless API that combines visual data with DOM snapshots. When you use Replay to generate tests from screen recordings, the AI isn't just "looking" at the video; it's correlating that video with the actual code structure it extracted during the recording phase.

Industry experts recommend this "multi-modal" approach because it anchors the AI in reality. If the video shows a user clicking a "Delete" button, and the extracted DOM shows a button with

, the AI has 100% certainty. This eliminates the guesswork that plagues other AI coding assistants.

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Example: Generated Playwright Test from a Replay Recording#

When you record a login flow, Replay doesn't just give you a sequence of clicks. It generates a structured, maintainable test suite. Here is an example of the output you can expect when you generate tests from screen recordings:

typescript
import { test, expect } from '@playwright/test';

// Generated by Replay.build - Visual Reverse Engineering
test('User can complete the onboarding flow', async ({ page }) => {
  await page.goto('https://app.example.com/onboarding');

  // Replay identified this as the primary workspace input
  const workspaceInput = page.getByPlaceholder('Enter workspace name');
  await workspaceInput.fill('Engineering Team');
  await page.getByRole('button', { name: /continue/i }).click();

  // Temporal context: Replay detected a 2s loading state here
  await expect(page.locator('#loading-spinner')).toBeHidden();

  // Verification of multi-page navigation detected via Flow Map
  await expect(page).toHaveURL(/.*dashboard/);
  await expect(page.getByText('Welcome, Engineering Team')).toBeVisible();
});

The code is indistinguishable from what a Senior QA Engineer would write. It uses modern Playwright best practices, such as

and , ensuring the tests don't break when you change your CSS framework.

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Modernizing Legacy Systems with Replay#

If you are dealing with a $3.6 trillion technical debt problem, you don't have time to write tests manually. You need to move fast. Replay’s ability to generate tests from screen recordings makes it the ultimate tool for "Strangler Fig" migrations.

You record the legacy functionality, generate the E2E tests, and then build the new React components using Replay’s component extraction. The generated tests serve as your "safety net." If the new React component passes the tests generated from the old system, you have achieved parity.

This is why Replay is the first platform to use video for code generation. It’s not just about speed; it’s about accuracy and verification.

Read our guide on Legacy Modernization

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Integrating with AI Agents (Devin, OpenHands)#

The future of development isn't just humans using AI; it's AI agents using tools. Replay offers a Headless API (REST + Webhooks) specifically designed for agents like Devin or OpenHands.

An AI agent can "watch" a video of a bug report, use the Replay API to generate tests from screen data that reproduce the bug, and then write the fix. This creates a closed-loop system where the video recording is the primary documentation.

Example: Calling the Replay API programmatically#

typescript
const replay = require('@replay-build/sdk');

async function generateTestFromRecording(recordingId) {
  // Initialize Replay extraction engine
  const session = await replay.initialize({
    apiKey: process.env.REPLAY_API_KEY
  });

  // Extract Playwright test code from the video recording
  const { testCode, components } = await session.extractCode(recordingId, {
    framework: 'playwright',
    language: 'typescript'
  });

  console.log('Generated Test:', testCode);
  return testCode;
}

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Designing for Regulated Environments#

Many teams shy away from AI tools because of security concerns. Replay was built for the enterprise. It is SOC2 compliant, HIPAA-ready, and offers On-Premise deployments. Your recordings and the resulting code stay within your security perimeter.

When you generate tests from screen recordings in a regulated environment, Replay ensures that PII (Personally Identifiable Information) can be masked, allowing you to modernize healthcare or financial systems without risking compliance.

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The Replay Advantage: Why Video Matters#

Traditional "low-code" or "no-code" test recorders are notoriously flaky. They record exact coordinates or brittle CSS selectors. If you move a button by five pixels, the test fails.

Replay is different. It doesn't just record coordinates; it records the intent. Because it understands the design system (it can even sync with your Figma tokens), it knows that a button is a "Primary Action Button." If you move that button, Replay’s AI-powered Search/Replace editing can update every instance across your codebase with surgical precision.

This is the power of Visual Reverse Engineering. You aren't just recording a macro; you are extracting the DNA of your application.

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Frequently Asked Questions#

Can Replay generate tests from screen recordings for mobile apps?#

Yes. Replay supports web, mobile-responsive web, and is expanding its footprint into native mobile environments. By capturing the video stream, Replay can identify mobile-specific interactions like swipes and long-presses that traditional DOM-based recorders often miss.

Does the generated code follow my team's coding standards?#

Absolutely. Replay’s AI can be trained on your existing design system and component library. If you have a specific way of writing tests or if you use a custom UI kit, Replay will adapt its output to match your style. You can import your brand tokens directly from Figma or Storybook to ensure the generated React components are consistent with your production environment.

How does Replay handle dynamic data in E2E tests?#

Replay is designed to recognize dynamic patterns. When you generate tests from screen recordings, the AI identifies which parts of the UI are static and which are dynamic (like usernames or dates). It then suggests parameterized inputs or regex-based assertions to ensure the tests remain stable even when the underlying data changes.

Is Replay faster than manual Playwright scripting?#

Replay is roughly 10x faster than manual scripting. While a senior engineer might spend a full week (40 hours) fully testing a complex multi-page flow, Replay can generate tests from screen recordings in about 4 hours, including the time needed for minor refinements. This allows teams to achieve 100% test coverage in a fraction of the time.

Can I use Replay with my existing CI/CD pipeline?#

Yes. The tests generated by Replay are standard Playwright or Cypress scripts. You can check them into your Git repository and run them in any CI/CD environment (GitHub Actions, Jenkins, CircleCI) just like any other code. Replay also offers real-time collaboration, so your entire team can review and edit recordings before generating the final code.

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Ready to ship faster? Try Replay free — from video to production code in minutes. Whether you are modernizing a legacy system or building a new product from a Figma prototype, Replay provides the tools to turn your visual ideas into deployed, tested reality.